docstr: Get all valid sharding specs from source_spec with one step transform, and
  accumulate communication cost on origin cost which will finally be used in auto sharding solver.
  Note:
      all-gather will eliminate a sharding dimension, all-to-all will keep sharding dimension same as before,
      and shard will add a sharding dimension. Therefore, the result of above operations are mutual exclusive,
      we could safely put them together.
  
  Argument:
      source_spec(ShardingSpec): the ShardingSpec of the source_spec.
      orig_cost(float): the original communication cost before this operation.
  
  Return:
      valid_spec_dict(Dict[ShardingSpec, float]): all valid sharding specs from source_spec with single all-to-all operation.
docstr_tok: ["Get","all","valid","sharding","specs","from","source_spec","with","one","step","transform",",","and","accumulate","communication","cost","on","origin","cost","which","will","finally","be","used","in","auto","sharding","solver",".","Note",":","all-gather","will","eliminate","a","sharding","dimension",",","all-to-all","will","keep","sharding","dimension","same","as","before",",","and","shard","will","add","a","sharding","dimension",".","Therefore",",","the","result","of","above","operations","are","mutual","exclusive",",","we","could","safely","put","them","together",".","Argument",":","source_spec","(","ShardingSpec",")",":","the","ShardingSpec","of","the","source_spec",".","orig_cost","(","float",")",":","the","original","communication","cost","before","this","operation",".","Return",":","valid_spec_dict","(","Dict","[","ShardingSpec",",","float","]",")",":","all","valid","sharding","specs","from","source_spec","with","single","all-to-all","operation","."]
code: def get_all_one_step_transform_spec(
      self, source_spec: ShardingSpec, orig_cost_dict
  ) -> Dict[ShardingSpec, float]:
      """
      Get all valid sharding specs from source_spec with one step transform, and
      accumulate communication cost on origin cost which will finally be used in auto sharding solver.
      Note:
          all-gather will eliminate a sharding dimension, all-to-all will keep sharding dimension same as before,
          and shard will add a sharding dimension. Therefore, the result of above operations are mutual exclusive,
          we could safely put them together.
  
      Argument:
          source_spec(ShardingSpec): the ShardingSpec of the source_spec.
          orig_cost(float): the original communication cost before this operation.
  
      Return:
          valid_spec_dict(Dict[ShardingSpec, float]): all valid sharding specs from source_spec with single all-to-all operation.
      """
      valid_spec_dict = {}
      valid_spec_dict.update(
          self.get_all_all_gather_spec(source_spec, orig_cost_dict)
      )
      valid_spec_dict.update(
          self.get_all_all_to_all_spec(source_spec, orig_cost_dict)
      )
      valid_spec_dict.update(
          self.get_all_shard_spec(source_spec, orig_cost_dict)
      )
      return valid_spec_dict
  
imports: import math
  from copy import deepcopy
  from dataclasses import dataclass
  from typing import Dict, List, Tuple
  import numpy
  import torch
  from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, TrainCycleItem
  from colossalai.context.singleton_meta import SingletonMeta
  from colossalai.tensor.sharding_spec import ShardingSpec, ShardingSpecException
  from colossalai.tensor.utils import all_gather_simulator, all_to_all_simulator, mix_gather_simulator, shard_simulator
  from .comm_spec import *
label_desc: Use image node_id 10 for calling the ShapeConsistencyManager obj's underlying member method code with example usage: obj.get_all_one_step_transform_spec(source_spec, orig_cost_dict) and returns: valid_spec_dict